code: def test_value(self):
      model = UNet(
          spatial_dims=2,
          in_channels=1,
          out_channels=3,
          channels=(16, 32, 64),
          strides=(2, 2),
          num_res_units=0,
      )
      with tempfile.TemporaryDirectory() as tempdir:
          torchscript_model = convert_to_torchscript(
              model=model,
              filename_or_obj=os.path.join(tempdir, "model.ts"),
              extra_files={"foo.txt": b"bar"},
              verify=True,
              inputs=[
                  torch.randn((16, 1, 32, 32), requires_grad=False)
              ],
              device="cuda" if torch.cuda.is_available() else "cpu",
              rtol=1e-3,
              atol=1e-4,
              optimize=None,
          )
          self.assertTrue(
              isinstance(torchscript_model, torch.nn.Module)
          )
  
file_path: MONAI/tests/test_convert_to_torchscript.py
imports: from __future__ import annotations
  import os
  import tempfile
  import unittest
  import torch
  from monai.networks import convert_to_torchscript
  from monai.networks.nets import UNet
label_desc: Use image node_id 1 for calling the TestConvertToTorchScript obj's underlying member method code with example usage: obj.test_value() without return types